If you can understand where the means for main effects and
interactions
are for a 2 (participant sex) x 2 (dress condition) x 2 (attitudes
toward
marriage) analysis of variance (ANOVA), then you should be able to
apply
this knowledge to other types of factorial designs. In this
example,
male
or female participants read about a marital rape victim who is dressed
somberly or suggestively and then made ratings of how responsible the
victim
was. An additional independent variable was created from
participants
responses
to an attitudes toward marriage scale and resulted in two conditions:
those
participants having more traditional attitudes toward marriage and
those
having more modern attitudes toward marriage. The results of the
analysis
appear below:

...This is the ANOVA Summary Table...

Recall that when you are writing up a results section you want
to cover
three things:
a) Tell the reader the analysis
that was conducted.
b) Whether the analysis was
significant
including the F-statement
(the statistical "proof").
c) Describe what the analysis
means in words. Be sure to include means and standard
deviations the
text or, in the case of a significant interaction, in
a table.

You should be able to see that there are significant main
effects for
sex of participant (SEX), dress condition (COND), and attitudes toward
marriage (ATMM). How do you know? If you examine the p-values
for
these main effects, then you can see that the values are less than
.05.
The F-statement and effect
size calculation ( r ) for sex of participant would be: F(1,
152) = 20.70, p < .001 (r = .35).

How would you write up the significant main effect for
participant
sex? Like this...

A 2
(sex of participant)
x 2 (dress condition) x 2 (attitudes toward marriage) analysis of
variance
(ANOVA) was calculated on participants' ratings of victim
responsibility. There
was a significant

Note:
The " 2 (sex of participant) x 2 (dress condition) x 2 (attitudes
toward
marriage) analysis of variance (ANOVA)" defines the variables and
analysis.
In subsequent analyses, for example,
maybe there are three dependent variables: Responsibility of the
victim, responsiblity of the attacker,
and
whether the attack was motivated by power. When you begin the
next
analysis section dealing with attacker responsibility,
you can start that section
with: "A 2 x 2 x 2 ANOVA was calculated..." because you have already
identified
the terms in the above section.

You would then continue on doing the same thing for any other
significant
main effects and interactions. If the analysis was not
significant,
then
you would still need to provide the F-statement, but you would
not
have to describe it. For example:

You would
deal with significant interactions in the same way. However, when
you
describe
the interaction, you do not include means and standard deviations,
because
those are often put in a table.
If you are using a figure to illustrate the interaction, then you would
include the means and standard deviations in the written description as
figures do not traditionally contain means and standard deviations.

For example:

There was a marginally significant Dress Condition x Attitudes toward
Marriage
interaction, F(1, 152) = 2.94, p =
.088 (r = .14). As can be seen in Table 1, in the
somberly dressed

condition, participants holding traditional attitudes toward
marriage assigned more responsibility to the victim than did
participants
holding more modern attitudes toward marriage.
In the suggestively

dressed condition, participants holding traditional
attitudes toward marriage assigned
more
responsibility to the victim than did participants holding more modern
attitudes toward marriage.

- OR -

There was a marginally significant Dress Condition x Attitudes toward
Marriage
interaction, F(1, 152) = 2.94, p =
.088 (r = .14). As can be seen in Figure 1, in the
somberly dressed

Of course, you will have to create a table with the
appropriate information.
Where would you find the appropriate information? Well, using the
descriptive
statistics table, you would look for that information under the three
main
collumns: SEX-COND-ATMM. For example, in the above interaction
description,
you would find the descriptive statistics across from
"total-somberly-traditional"
(M = 5.25), "total-somberly-modern" (M = 3.85),
"total-suggestive-traditional"
(M = 5.69), and "total-suggestive-modern" (M = 5.42).

Test Yourself:

Write-up the
significant main effects for "COND" and "ATMM." In addition, attempt
the
write-up for the significant
interaction. The write-up should conform to APA style. If
you choose
to undertake this "test," then bring it by my office once you have
finished
and we will go over it.